Finite-Time Stabilization of Fuzzy Spatiotemporal Competitive Neural Networks With Hybrid Time-Varying Delays

Abstract

This paper focuses on the finite-time stabilization problem for fuzzy spatiotemporal competitive neural networks (FSCNNs) with discrete and distributed delays. First, the differentiable conditions for discrete and finite distributed delays in FSCNNs are removed, and the constraints of the kernel function in infinite distributed delays are weakened. Then, a novel partial differential inequality is proposed to handle the spatial diffusions, which relaxes the restriction for symmetric around the origin of the bounded spatial domain. To stabilize FSCNNs within a finite time, a novel control strategy without delay-dependent terms is established. Moreover, different from the existing works, a more succinct Lyapunov functional is constructed, which does not need to include multiple integral type functional terms to eliminate the influence of the hybrid delays. By virtue of the comparison method and inequality techniques, several sufficient criteria are deduced to guarantee the finite-time stabilization of FSCNNs. Finally, simulations are presented to illustrate the feasibility and effectiveness of the theoretical results.